The Machine Learning Pipeline on AWS - April 16

Schedule

Live Online Training

Date: April 16, 17, 18, & 19, 2024

Time: 8:45am - 4:45pm EST

• Enrollment window closes 48 hours prior to class start.
• Course will be published and able to be viewed one business day in advance to start date.

What you'll learn

This course explores how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine-learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

In this course, you will learn to:

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific business problem
• Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
• Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
• Apply machine learning to a real-life business problem after the course is complete
  

The Machine Learning Pipeline on AWS Course Outline

Ready to take your cloud knowledge to the next level? Enroll now.

Who should take this course?

This course is intended for:

• Developers
• Solutions Architects
• Data Engineers
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Recommended Prerequisites

We recommend that attendees of this course have:

• Basic knowledge of Python programming language
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Basic experience working in a Jupyter Notebook environment
  
  
  

Relevant Certification Path

To earn this relevant certification, you will need to take and pass the AWS Certified Machine Learning – Specialty exam. The prices for courses do not include exam costs for certifications.

Information and Policy

Frequently Asked Questions:

• Certification Prep Course FAQ

Refund Policy:

• Refund requests submitted 72 hours or more prior to class start will result in a full refund.
• Refunds will not be remitted less than 72 hours in advance of class or for non-attendance.